






"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case : List[str]     		=		logging.get_logger()


def 							A						(				__snake_case: int ,     __snake_case: str ,     __snake_case: LevitConfig ,     __snake_case: Path ,     __snake_case: bool = True  )    ->		Any:

       """simple docstring"""





       print(F"""Converting {name}..."""  )

       with torch.no_grad():
              if hidden_sizes == 1_2_8:
                     if name[-1] == "S":
                            __magic_name__      				=    timm.create_model('levit_128s' ,     pretrained=__snake_case  )
                     else:
                            __magic_name__      				=    timm.create_model('levit_128' ,     pretrained=__snake_case  )
              if hidden_sizes == 1_9_2:
                     __magic_name__      				=    timm.create_model('levit_192' ,     pretrained=__snake_case  )
              if hidden_sizes == 2_5_6:
                     __magic_name__      				=    timm.create_model('levit_256' ,     pretrained=__snake_case  )
              if hidden_sizes == 3_8_4:
                     __magic_name__      				=    timm.create_model('levit_384' ,     pretrained=__snake_case  )

              from_model.eval()
              __magic_name__      				=    LevitForImageClassificationWithTeacher(__snake_case  ).eval()
              __magic_name__      				=    OrderedDict()

              __magic_name__      				=    from_model.state_dict()
              __magic_name__      				=    list(from_model.state_dict().keys()  )
              __magic_name__      				=    list(our_model.state_dict().keys()  )
              print(len(__snake_case  ) ,     len(__snake_case  )  )
              for i in range(len(__snake_case  )  ):
                     __magic_name__      				=    weights[og_keys[i]]
              our_model.load_state_dict(__snake_case  )

              __magic_name__      				=    torch.randn((2, 3, 2_2_4, 2_2_4)  )
              __magic_name__      				=    from_model(__snake_case  )
              __magic_name__      				=    our_model(__snake_case  ).logits

       assert torch.allclose(__snake_case ,     __snake_case  ), "The model logits don't match the original one."

       __magic_name__      				=    name
       print(__snake_case  )

       if push_to_hub:
              our_model.save_pretrained(save_directory / checkpoint_name  )
              __magic_name__      				=    LevitImageProcessor()
              image_processor.save_pretrained(save_directory / checkpoint_name  )

              print(F"""Pushed {checkpoint_name}"""  )


def 							A						(				__snake_case: Path ,     __snake_case: str = None ,     __snake_case: bool = True  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    'imagenet-1k-id2label.json'
       __magic_name__      				=    1_0_0_0
       __magic_name__      				=    (1, num_labels)

       __magic_name__      				=    'huggingface/label-files'
       __magic_name__      				=    num_labels
       __magic_name__      				=    json.load(open(hf_hub_download(__snake_case ,     __snake_case ,     repo_type='dataset'  ) ,     'r'  )  )
       __magic_name__      				=    {int(__snake_case  ): v for k, v in idalabel.items()}

       __magic_name__      				=    idalabel
       __magic_name__      				=    {v: k for k, v in idalabel.items()}

       __magic_name__      				=    partial(__snake_case ,     num_labels=__snake_case ,     idalabel=__snake_case ,     labelaid=__snake_case  )

       __magic_name__      				=    {
           'levit-128S': 1_2_8,
           'levit-128': 1_2_8,
           'levit-192': 1_9_2,
           'levit-256': 2_5_6,
           'levit-384': 3_8_4,
       }

       __magic_name__      				=    {
           'levit-128S': ImageNetPreTrainedConfig(
               hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,     num_attention_heads=[4, 6, 8] ,     depths=[2, 3, 4] ,     key_dim=[1_6, 1_6, 1_6] ,     drop_path_rate=0 ,     ),
           'levit-128': ImageNetPreTrainedConfig(
               hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,     num_attention_heads=[4, 8, 1_2] ,     depths=[4, 4, 4] ,     key_dim=[1_6, 1_6, 1_6] ,     drop_path_rate=0 ,     ),
           'levit-192': ImageNetPreTrainedConfig(
               hidden_sizes=[1_9_2, 2_8_8, 3_8_4] ,     num_attention_heads=[3, 5, 6] ,     depths=[4, 4, 4] ,     key_dim=[3_2, 3_2, 3_2] ,     drop_path_rate=0 ,     ),
           'levit-256': ImageNetPreTrainedConfig(
               hidden_sizes=[2_5_6, 3_8_4, 5_1_2] ,     num_attention_heads=[4, 6, 8] ,     depths=[4, 4, 4] ,     key_dim=[3_2, 3_2, 3_2] ,     drop_path_rate=0 ,     ),
           'levit-384': ImageNetPreTrainedConfig(
               hidden_sizes=[3_8_4, 5_1_2, 7_6_8] ,     num_attention_heads=[6, 9, 1_2] ,     depths=[4, 4, 4] ,     key_dim=[3_2, 3_2, 3_2] ,     drop_path_rate=0.1 ,     ),
       }

       if model_name:
              convert_weight_and_push(
                  names_to_hidden_sizes[model_name] ,     __snake_case ,     names_to_config[model_name] ,     __snake_case ,     __snake_case  )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(names_to_hidden_sizes[model_name] ,     __snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )
       return config, expected_shape


if __name__ == "__main__":
 snake_case : Optional[int]     		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""levit-dump-folder/""",
     type=Path,
     required=False,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
 parser.add_argument(
     """--no-push_to_hub""",
     dest="""push_to_hub""",
     action="""store_false""",
     help="""Do not push model and image processor to the hub""",
 )

 snake_case : str     		=		parser.parse_args()
 snake_case : Path     		=		args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()